Assessment of climate change impact on probable maximum floods in a tropical catchment

Sammen, Saad Sh.; Mohammed, Thamer Ahmed; Ghazali, Abdul Halim; Sidek, Lariyah Mohd; Shahid, Shamsuddin; Abba, S.I.; Malik, Anurag; Al‐Ansari, Nadhir

doi:10.1007/s00704-022-03925-9

Cited by 7 publications

(3 citation statements)

References 51 publications

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“…The integration of deep learning with the CSA thus presents a robust and innovative approach to optimizing the performance of NF-RO desalination processes, contributing to advancements in addressing global water scarcity challenges. The performance criteria used to calculate the models' accuracy are presented in the following equations [38,39]:…”

Section: Experimental Methodsmentioning

confidence: 99%

Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm

Abba,

Usman,

Abdulazeez

et al. 2023

Water

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The need for reliable, state-of-the-art environmental investigations and pioneering approaches to address pressing ecological dilemmas and to nurture the sustainable development goals (SDGs) cannot be overstated. With the power to revolutionize desalination processes, artificial intelligence (AI) models hold the potential to address global water scarcity challenges and contribute to a more sustainable and resilient future. The realm of desalination has exhibited a mounting inclination toward modeling the efficacy of the hybrid nanofiltration/reverse osmosis (NF–RO) process. In this research, the performance of NF–RO based on permeate conductivity was developed using deep learning long short-term memory (LSTM) integrated with an optimized metaheuristic crow search algorithm (CSA) (LSTM-CSA). Before model development, an uncertainty Monte Carlo simulation was adopted to evaluate the uncertainty attributed to the prediction. The results based on several performance statistical criteria (root mean square error (RMSE) and mean absolute error (MAE)) demonstrated the reliability of both LSTM (RMSE = 0.1971, MAE = 0.2022) and the LSTM-CSA (RMSE = 0.1890, MAE = 0.1420), with the latter achieving the highest accuracy. The accuracy was also evaluated using new 2D graphical visualization, including a cumulative distribution function (CDF) and fan plot to justify the other evaluation indicators such as standard deviation and determination coefficients. The outcomes proved that AI could optimize energy usage, identify energy-saving opportunities, and suggest more sustainable operating strategies. Additionally, AI can aid in developing advanced brine treatment techniques, facilitating the extraction of valuable resources from the brine, thus minimizing waste and maximizing resource utilization.

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Section: Experimental Methodsmentioning

confidence: 99%

Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm

Abba,

Usman,

Abdulazeez

et al. 2023

Water

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“…The latest Intergovernmental Panel on Climate Change (IPCC) report has highlighted thought‐provoking conclusions, such as the unprecedented warming of the climate due to human influence (IPCC, 2021). It has been proven that water‐related climate hazards and multiple risks to ecosystems and human settlements intensify with global warming (Sammen et al, 2022; Tabari, 2021), particularly in coastal cities at low elevations (Toimil et al, 2020; Vousdoukas et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A stacked ensemble learning and non‐homogeneous hidden Markov model for daily precipitation downscaling and projection

Jiang,

Cioffi,

Conticello

et al. 2023

Hydrological Processes

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Global circulation models (GCMs) are routinely used to project future climate conditions worldwide, such as temperature and precipitation. However, inputs with a finer resolution are required to drive impact‐related models at local scales. The non‐homogeneous hidden Markov model (NHMM) is a widely used algorithm for the precipitation statistical downscaling for GCMs. To improve the accuracy of the traditional NHMM in reproducing spatiotemporal precipitation features of specific geographic sites, especially extreme precipitation, we developed a new precipitation downscaling framework. This hierarchical model includes two levels: (1) establishing an ensemble learning model to predict the occurrence probabilities for different levels of daily precipitation aggregated at multiple sites and (2) constructing a NHMM downscaling scheme of daily amount at the scale of a single rain gauge using the outputs of ensemble learning model as predictors. As the results obtained for the case study in the central‐eastern China (CEC), show that our downscaling model is highly efficient and performs better than the NHMM in simulating precipitation variability and extreme precipitation. Finally, our projections indicate that CEC may experience increased precipitation in the future. Compared with ~26 years (1990–2015), the extreme precipitation frequency and amount would significantly increase by 21.9%–48.1% and 12.3%–38.3%, respectively, by the late century (2075–2100) under the Shared Socioeconomic Pathway 585 climate scenario.

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“…It has caused worldwide temporal and spatial changes in climate variables [2][3][4][5][6]. It has also affected the frequency and severity of natural disasters, including droughts [7][8][9][10][11], heatwaves [12,13], and flooding [14][15][16]. Numerous industries have also been impacted by climate change, including water resources [17][18][19], agriculture [20][21][22], energy [23,24], and health [25,26].…”

Section: Introductionmentioning

confidence: 99%

Comparison between CMIP5 and CMIP6 Models over MENA Region Using Historical Simulations and Future Projections

Hamed

Nashwan

Shiru³

et al. 2022

Sustainability

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The study evaluated the ability of 11 global climate models of the latest two versions of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) to simulate observed (1965–2005) rainfall, maximum (Tmax) and minimum (Tmin) temperatures, mean eastward (uas) and northward (vas) wind speed, and mean surface pressure. It also evaluated relative uncertainty in projections of climate variables using those two CMIPs. The European reanalysis (ERA5) data were used as the reference to evaluate the performance of the GCMs and their mean and median multimodel ensembles (MME). The study revealed less bias in CMIP6 GCMs than CMIP5 GCMs in simulating most climate variables. The biases in rainfall, Tmax, Tmin, uas, vas, and surface pressure were −55 mm, 0.28 °C, −0.11 °C, −0.25 m/s, −0.06 m/s, and −0.038 Kpa for CMIP6 compared to −65 mm, 0.07 °C, −0.87 °C, −0.41 m/s, −0.05 m/s, and 0.063 Kpa for CMIP5. The uncertainty in CMIP6 projections of rainfall, Tmax, Tmin, uas, vas, and wind speed was relative more narrow than those for CMIP5. The projections showed a higher increase in Tmin than Tmax by 0.64 °C, especially in the central region. Besides, rainfall in most parts of MENA would increase; however, it might decrease by 50 mm in the coastal regions. The study revealed the better ability of CMIP6 GCMs for a wide range of climatic studies.

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Assessment of climate change impact on probable maximum floods in a tropical catchment

Cited by 7 publications

References 51 publications

Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm

Integrated Modeling of Hybrid Nanofiltration/Reverse Osmosis Desalination Plant Using Deep Learning-Based Crow Search Optimization Algorithm

A stacked ensemble learning and non‐homogeneous hidden Markov model for daily precipitation downscaling and projection

Comparison between CMIP5 and CMIP6 Models over MENA Region Using Historical Simulations and Future Projections

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